Benchmarking Deep Learning-Based Low-Dose CT Image Denoising Algorithms

Elias Eulig, Björn Ommer, Marc Kachelriess

Abstract:

Long lasting efforts have been made to reduce radi-
ation dose and thus the potential radiation risk to the patient for
computed tomography acquisitions without severe deterioration
of image quality. To this end, numerous reconstruction and
noise reduction algorithms have been developed, many of which
are based on iterative reconstruction techniques, incorporating
prior knowledge in the projection or image domain. Recently,
deep learning-based methods became increasingly popular and
a multitude of papers claim ever improving performance both
quantitatively and qualitatively. In this work, we find that the
lack of a common benchmark setup and flaws in the experimental
setup of many publications hinder verifiability of those claims.
We propose a benchmark setup to overcome those flaws and
improve reproducibility and verifiability of experimental results
in the field. In a comprehensive and fair evaluation of several deep
learning-based low dose CT denoising algorithms, we find that
most methods perform statistically similar and improvements
over the past six years have been marginal at best.